{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-06-17T00:24:49.835982400Z",
     "start_time": "2024-06-17T00:24:32.661100Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from models.vit import vit_small\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "class net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.encoder = vit_small()\n",
    "        self.hooks = []\n",
    "        for name,module in self.encoder.named_modules():\n",
    "            if 'attn_drop' in name:\n",
    "                self.hooks.append(module.register_forward_hook(self.get_attention))\n",
    "        self.attentions = []\n",
    "\n",
    "    def get_attention(self, module, input, output):\n",
    "        self.attentions.append(output.cpu())\n",
    "\n",
    "    def forward(self,x):\n",
    "        return  self.encoder(x)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-17T00:24:54.216448400Z",
     "start_time": "2024-06-17T00:24:54.181033700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "att_net = net()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-17T00:24:59.601775300Z",
     "start_time": "2024-06-17T00:24:57.268806100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "checkpoint = \"F:\\checkpoint\\ViT-S-16\\miniImageNet\\checkpoint1600.pth\""
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-17T00:25:01.970673Z",
     "start_time": "2024-06-17T00:25:01.938924500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['encoder.cls_token', 'encoder.pos_embed', 'encoder.patch_embed.proj.weight', 'encoder.patch_embed.proj.bias', 'encoder.blocks.0.norm1.weight', 'encoder.blocks.0.norm1.bias', 'encoder.blocks.0.attn.qkv.weight', 'encoder.blocks.0.attn.qkv.bias', 'encoder.blocks.0.attn.proj.weight', 'encoder.blocks.0.attn.proj.bias', 'encoder.blocks.0.norm2.weight', 'encoder.blocks.0.norm2.bias', 'encoder.blocks.0.mlp.fc1.weight', 'encoder.blocks.0.mlp.fc1.bias', 'encoder.blocks.0.mlp.fc2.weight', 'encoder.blocks.0.mlp.fc2.bias', 'encoder.blocks.1.norm1.weight', 'encoder.blocks.1.norm1.bias', 'encoder.blocks.1.attn.qkv.weight', 'encoder.blocks.1.attn.qkv.bias', 'encoder.blocks.1.attn.proj.weight', 'encoder.blocks.1.attn.proj.bias', 'encoder.blocks.1.norm2.weight', 'encoder.blocks.1.norm2.bias', 'encoder.blocks.1.mlp.fc1.weight', 'encoder.blocks.1.mlp.fc1.bias', 'encoder.blocks.1.mlp.fc2.weight', 'encoder.blocks.1.mlp.fc2.bias', 'encoder.blocks.2.norm1.weight', 'encoder.blocks.2.norm1.bias', 'encoder.blocks.2.attn.qkv.weight', 'encoder.blocks.2.attn.qkv.bias', 'encoder.blocks.2.attn.proj.weight', 'encoder.blocks.2.attn.proj.bias', 'encoder.blocks.2.norm2.weight', 'encoder.blocks.2.norm2.bias', 'encoder.blocks.2.mlp.fc1.weight', 'encoder.blocks.2.mlp.fc1.bias', 'encoder.blocks.2.mlp.fc2.weight', 'encoder.blocks.2.mlp.fc2.bias', 'encoder.blocks.3.norm1.weight', 'encoder.blocks.3.norm1.bias', 'encoder.blocks.3.attn.qkv.weight', 'encoder.blocks.3.attn.qkv.bias', 'encoder.blocks.3.attn.proj.weight', 'encoder.blocks.3.attn.proj.bias', 'encoder.blocks.3.norm2.weight', 'encoder.blocks.3.norm2.bias', 'encoder.blocks.3.mlp.fc1.weight', 'encoder.blocks.3.mlp.fc1.bias', 'encoder.blocks.3.mlp.fc2.weight', 'encoder.blocks.3.mlp.fc2.bias', 'encoder.blocks.4.norm1.weight', 'encoder.blocks.4.norm1.bias', 'encoder.blocks.4.attn.qkv.weight', 'encoder.blocks.4.attn.qkv.bias', 'encoder.blocks.4.attn.proj.weight', 'encoder.blocks.4.attn.proj.bias', 'encoder.blocks.4.norm2.weight', 'encoder.blocks.4.norm2.bias', 'encoder.blocks.4.mlp.fc1.weight', 'encoder.blocks.4.mlp.fc1.bias', 'encoder.blocks.4.mlp.fc2.weight', 'encoder.blocks.4.mlp.fc2.bias', 'encoder.blocks.5.norm1.weight', 'encoder.blocks.5.norm1.bias', 'encoder.blocks.5.attn.qkv.weight', 'encoder.blocks.5.attn.qkv.bias', 'encoder.blocks.5.attn.proj.weight', 'encoder.blocks.5.attn.proj.bias', 'encoder.blocks.5.norm2.weight', 'encoder.blocks.5.norm2.bias', 'encoder.blocks.5.mlp.fc1.weight', 'encoder.blocks.5.mlp.fc1.bias', 'encoder.blocks.5.mlp.fc2.weight', 'encoder.blocks.5.mlp.fc2.bias', 'encoder.blocks.6.norm1.weight', 'encoder.blocks.6.norm1.bias', 'encoder.blocks.6.attn.qkv.weight', 'encoder.blocks.6.attn.qkv.bias', 'encoder.blocks.6.attn.proj.weight', 'encoder.blocks.6.attn.proj.bias', 'encoder.blocks.6.norm2.weight', 'encoder.blocks.6.norm2.bias', 'encoder.blocks.6.mlp.fc1.weight', 'encoder.blocks.6.mlp.fc1.bias', 'encoder.blocks.6.mlp.fc2.weight', 'encoder.blocks.6.mlp.fc2.bias', 'encoder.blocks.7.norm1.weight', 'encoder.blocks.7.norm1.bias', 'encoder.blocks.7.attn.qkv.weight', 'encoder.blocks.7.attn.qkv.bias', 'encoder.blocks.7.attn.proj.weight', 'encoder.blocks.7.attn.proj.bias', 'encoder.blocks.7.norm2.weight', 'encoder.blocks.7.norm2.bias', 'encoder.blocks.7.mlp.fc1.weight', 'encoder.blocks.7.mlp.fc1.bias', 'encoder.blocks.7.mlp.fc2.weight', 'encoder.blocks.7.mlp.fc2.bias', 'encoder.blocks.8.norm1.weight', 'encoder.blocks.8.norm1.bias', 'encoder.blocks.8.attn.qkv.weight', 'encoder.blocks.8.attn.qkv.bias', 'encoder.blocks.8.attn.proj.weight', 'encoder.blocks.8.attn.proj.bias', 'encoder.blocks.8.norm2.weight', 'encoder.blocks.8.norm2.bias', 'encoder.blocks.8.mlp.fc1.weight', 'encoder.blocks.8.mlp.fc1.bias', 'encoder.blocks.8.mlp.fc2.weight', 'encoder.blocks.8.mlp.fc2.bias', 'encoder.blocks.9.norm1.weight', 'encoder.blocks.9.norm1.bias', 'encoder.blocks.9.attn.qkv.weight', 'encoder.blocks.9.attn.qkv.bias', 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'encoder.blocks.11.mlp.fc1.weight', 'encoder.blocks.11.mlp.fc1.bias', 'encoder.blocks.11.mlp.fc2.weight', 'encoder.blocks.11.mlp.fc2.bias', 'encoder.norm.weight', 'encoder.norm.bias', 'encoder.head.weight', 'encoder.head.bias'])\n",
      "odict_keys(['backbone.cls_token', 'backbone.pos_embed', 'backbone.patch_embed.proj.weight', 'backbone.patch_embed.proj.bias', 'backbone.blocks.0.norm1.weight', 'backbone.blocks.0.norm1.bias', 'backbone.blocks.0.attn.qkv.weight', 'backbone.blocks.0.attn.qkv.bias', 'backbone.blocks.0.attn.proj.weight', 'backbone.blocks.0.attn.proj.bias', 'backbone.blocks.0.norm2.weight', 'backbone.blocks.0.norm2.bias', 'backbone.blocks.0.mlp.fc1.weight', 'backbone.blocks.0.mlp.fc1.bias', 'backbone.blocks.0.mlp.fc2.weight', 'backbone.blocks.0.mlp.fc2.bias', 'backbone.blocks.1.norm1.weight', 'backbone.blocks.1.norm1.bias', 'backbone.blocks.1.attn.qkv.weight', 'backbone.blocks.1.attn.qkv.bias', 'backbone.blocks.1.attn.proj.weight', 'backbone.blocks.1.attn.proj.bias', 'backbone.blocks.1.norm2.weight', 'backbone.blocks.1.norm2.bias', 'backbone.blocks.1.mlp.fc1.weight', 'backbone.blocks.1.mlp.fc1.bias', 'backbone.blocks.1.mlp.fc2.weight', 'backbone.blocks.1.mlp.fc2.bias', 'backbone.blocks.2.norm1.weight', 'backbone.blocks.2.norm1.bias', 'backbone.blocks.2.attn.qkv.weight', 'backbone.blocks.2.attn.qkv.bias', 'backbone.blocks.2.attn.proj.weight', 'backbone.blocks.2.attn.proj.bias', 'backbone.blocks.2.norm2.weight', 'backbone.blocks.2.norm2.bias', 'backbone.blocks.2.mlp.fc1.weight', 'backbone.blocks.2.mlp.fc1.bias', 'backbone.blocks.2.mlp.fc2.weight', 'backbone.blocks.2.mlp.fc2.bias', 'backbone.blocks.3.norm1.weight', 'backbone.blocks.3.norm1.bias', 'backbone.blocks.3.attn.qkv.weight', 'backbone.blocks.3.attn.qkv.bias', 'backbone.blocks.3.attn.proj.weight', 'backbone.blocks.3.attn.proj.bias', 'backbone.blocks.3.norm2.weight', 'backbone.blocks.3.norm2.bias', 'backbone.blocks.3.mlp.fc1.weight', 'backbone.blocks.3.mlp.fc1.bias', 'backbone.blocks.3.mlp.fc2.weight', 'backbone.blocks.3.mlp.fc2.bias', 'backbone.blocks.4.norm1.weight', 'backbone.blocks.4.norm1.bias', 'backbone.blocks.4.attn.qkv.weight', 'backbone.blocks.4.attn.qkv.bias', 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'backbone.blocks.9.norm1.weight', 'backbone.blocks.9.norm1.bias', 'backbone.blocks.9.attn.qkv.weight', 'backbone.blocks.9.attn.qkv.bias', 'backbone.blocks.9.attn.proj.weight', 'backbone.blocks.9.attn.proj.bias', 'backbone.blocks.9.norm2.weight', 'backbone.blocks.9.norm2.bias', 'backbone.blocks.9.mlp.fc1.weight', 'backbone.blocks.9.mlp.fc1.bias', 'backbone.blocks.9.mlp.fc2.weight', 'backbone.blocks.9.mlp.fc2.bias', 'backbone.blocks.10.norm1.weight', 'backbone.blocks.10.norm1.bias', 'backbone.blocks.10.attn.qkv.weight', 'backbone.blocks.10.attn.qkv.bias', 'backbone.blocks.10.attn.proj.weight', 'backbone.blocks.10.attn.proj.bias', 'backbone.blocks.10.norm2.weight', 'backbone.blocks.10.norm2.bias', 'backbone.blocks.10.mlp.fc1.weight', 'backbone.blocks.10.mlp.fc1.bias', 'backbone.blocks.10.mlp.fc2.weight', 'backbone.blocks.10.mlp.fc2.bias', 'backbone.blocks.11.norm1.weight', 'backbone.blocks.11.norm1.bias', 'backbone.blocks.11.attn.qkv.weight', 'backbone.blocks.11.attn.qkv.bias', 'backbone.blocks.11.attn.proj.weight', 'backbone.blocks.11.attn.proj.bias', 'backbone.blocks.11.norm2.weight', 'backbone.blocks.11.norm2.bias', 'backbone.blocks.11.mlp.fc1.weight', 'backbone.blocks.11.mlp.fc1.bias', 'backbone.blocks.11.mlp.fc2.weight', 'backbone.blocks.11.mlp.fc2.bias', 'backbone.norm.weight', 'backbone.norm.bias', 'head.mlp.0.weight', 'head.mlp.0.bias', 'head.mlp.2.weight', 'head.mlp.2.bias', 'head.mlp.4.weight', 'head.mlp.4.bias', 'head.last_layer.weight_g', 'head.last_layer.weight_v', 'head.last_layer2.weight_g', 'head.last_layer2.weight_v'])\n",
      "dict_keys(['encoder.cls_token', 'encoder.pos_embed', 'encoder.patch_embed.proj.weight', 'encoder.patch_embed.proj.bias', 'encoder.blocks.0.norm1.weight', 'encoder.blocks.0.norm1.bias', 'encoder.blocks.0.attn.qkv.weight', 'encoder.blocks.0.attn.qkv.bias', 'encoder.blocks.0.attn.proj.weight', 'encoder.blocks.0.attn.proj.bias', 'encoder.blocks.0.norm2.weight', 'encoder.blocks.0.norm2.bias', 'encoder.blocks.0.mlp.fc1.weight', 'encoder.blocks.0.mlp.fc1.bias', 'encoder.blocks.0.mlp.fc2.weight', 'encoder.blocks.0.mlp.fc2.bias', 'encoder.blocks.1.norm1.weight', 'encoder.blocks.1.norm1.bias', 'encoder.blocks.1.attn.qkv.weight', 'encoder.blocks.1.attn.qkv.bias', 'encoder.blocks.1.attn.proj.weight', 'encoder.blocks.1.attn.proj.bias', 'encoder.blocks.1.norm2.weight', 'encoder.blocks.1.norm2.bias', 'encoder.blocks.1.mlp.fc1.weight', 'encoder.blocks.1.mlp.fc1.bias', 'encoder.blocks.1.mlp.fc2.weight', 'encoder.blocks.1.mlp.fc2.bias', 'encoder.blocks.2.norm1.weight', 'encoder.blocks.2.norm1.bias', 'encoder.blocks.2.attn.qkv.weight', 'encoder.blocks.2.attn.qkv.bias', 'encoder.blocks.2.attn.proj.weight', 'encoder.blocks.2.attn.proj.bias', 'encoder.blocks.2.norm2.weight', 'encoder.blocks.2.norm2.bias', 'encoder.blocks.2.mlp.fc1.weight', 'encoder.blocks.2.mlp.fc1.bias', 'encoder.blocks.2.mlp.fc2.weight', 'encoder.blocks.2.mlp.fc2.bias', 'encoder.blocks.3.norm1.weight', 'encoder.blocks.3.norm1.bias', 'encoder.blocks.3.attn.qkv.weight', 'encoder.blocks.3.attn.qkv.bias', 'encoder.blocks.3.attn.proj.weight', 'encoder.blocks.3.attn.proj.bias', 'encoder.blocks.3.norm2.weight', 'encoder.blocks.3.norm2.bias', 'encoder.blocks.3.mlp.fc1.weight', 'encoder.blocks.3.mlp.fc1.bias', 'encoder.blocks.3.mlp.fc2.weight', 'encoder.blocks.3.mlp.fc2.bias', 'encoder.blocks.4.norm1.weight', 'encoder.blocks.4.norm1.bias', 'encoder.blocks.4.attn.qkv.weight', 'encoder.blocks.4.attn.qkv.bias', 'encoder.blocks.4.attn.proj.weight', 'encoder.blocks.4.attn.proj.bias', 'encoder.blocks.4.norm2.weight', 'encoder.blocks.4.norm2.bias', 'encoder.blocks.4.mlp.fc1.weight', 'encoder.blocks.4.mlp.fc1.bias', 'encoder.blocks.4.mlp.fc2.weight', 'encoder.blocks.4.mlp.fc2.bias', 'encoder.blocks.5.norm1.weight', 'encoder.blocks.5.norm1.bias', 'encoder.blocks.5.attn.qkv.weight', 'encoder.blocks.5.attn.qkv.bias', 'encoder.blocks.5.attn.proj.weight', 'encoder.blocks.5.attn.proj.bias', 'encoder.blocks.5.norm2.weight', 'encoder.blocks.5.norm2.bias', 'encoder.blocks.5.mlp.fc1.weight', 'encoder.blocks.5.mlp.fc1.bias', 'encoder.blocks.5.mlp.fc2.weight', 'encoder.blocks.5.mlp.fc2.bias', 'encoder.blocks.6.norm1.weight', 'encoder.blocks.6.norm1.bias', 'encoder.blocks.6.attn.qkv.weight', 'encoder.blocks.6.attn.qkv.bias', 'encoder.blocks.6.attn.proj.weight', 'encoder.blocks.6.attn.proj.bias', 'encoder.blocks.6.norm2.weight', 'encoder.blocks.6.norm2.bias', 'encoder.blocks.6.mlp.fc1.weight', 'encoder.blocks.6.mlp.fc1.bias', 'encoder.blocks.6.mlp.fc2.weight', 'encoder.blocks.6.mlp.fc2.bias', 'encoder.blocks.7.norm1.weight', 'encoder.blocks.7.norm1.bias', 'encoder.blocks.7.attn.qkv.weight', 'encoder.blocks.7.attn.qkv.bias', 'encoder.blocks.7.attn.proj.weight', 'encoder.blocks.7.attn.proj.bias', 'encoder.blocks.7.norm2.weight', 'encoder.blocks.7.norm2.bias', 'encoder.blocks.7.mlp.fc1.weight', 'encoder.blocks.7.mlp.fc1.bias', 'encoder.blocks.7.mlp.fc2.weight', 'encoder.blocks.7.mlp.fc2.bias', 'encoder.blocks.8.norm1.weight', 'encoder.blocks.8.norm1.bias', 'encoder.blocks.8.attn.qkv.weight', 'encoder.blocks.8.attn.qkv.bias', 'encoder.blocks.8.attn.proj.weight', 'encoder.blocks.8.attn.proj.bias', 'encoder.blocks.8.norm2.weight', 'encoder.blocks.8.norm2.bias', 'encoder.blocks.8.mlp.fc1.weight', 'encoder.blocks.8.mlp.fc1.bias', 'encoder.blocks.8.mlp.fc2.weight', 'encoder.blocks.8.mlp.fc2.bias', 'encoder.blocks.9.norm1.weight', 'encoder.blocks.9.norm1.bias', 'encoder.blocks.9.attn.qkv.weight', 'encoder.blocks.9.attn.qkv.bias', 'encoder.blocks.9.attn.proj.weight', 'encoder.blocks.9.attn.proj.bias', 'encoder.blocks.9.norm2.weight', 'encoder.blocks.9.norm2.bias', 'encoder.blocks.9.mlp.fc1.weight', 'encoder.blocks.9.mlp.fc1.bias', 'encoder.blocks.9.mlp.fc2.weight', 'encoder.blocks.9.mlp.fc2.bias', 'encoder.blocks.10.norm1.weight', 'encoder.blocks.10.norm1.bias', 'encoder.blocks.10.attn.qkv.weight', 'encoder.blocks.10.attn.qkv.bias', 'encoder.blocks.10.attn.proj.weight', 'encoder.blocks.10.attn.proj.bias', 'encoder.blocks.10.norm2.weight', 'encoder.blocks.10.norm2.bias', 'encoder.blocks.10.mlp.fc1.weight', 'encoder.blocks.10.mlp.fc1.bias', 'encoder.blocks.10.mlp.fc2.weight', 'encoder.blocks.10.mlp.fc2.bias', 'encoder.blocks.11.norm1.weight', 'encoder.blocks.11.norm1.bias', 'encoder.blocks.11.attn.qkv.weight', 'encoder.blocks.11.attn.qkv.bias', 'encoder.blocks.11.attn.proj.weight', 'encoder.blocks.11.attn.proj.bias', 'encoder.blocks.11.norm2.weight', 'encoder.blocks.11.norm2.bias', 'encoder.blocks.11.mlp.fc1.weight', 'encoder.blocks.11.mlp.fc1.bias', 'encoder.blocks.11.mlp.fc2.weight', 'encoder.blocks.11.mlp.fc2.bias', 'encoder.norm.weight', 'encoder.norm.bias'])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<All keys matched successfully>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dict = att_net.state_dict()\n",
    "print(model_dict.keys())\n",
    "pretrained_dict = torch.load(checkpoint, map_location='cpu')['teacher']\n",
    "print(pretrained_dict.keys())\n",
    "pretrained_dict = {k.replace('backbone', 'encoder'): v for k, v in pretrained_dict.items()}\n",
    "pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}\n",
    "print(pretrained_dict.keys())\n",
    "model_dict.update(pretrained_dict)\n",
    "att_net.load_state_dict(model_dict)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-17T00:25:10.740849700Z",
     "start_time": "2024-06-17T00:25:07.749656Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "net(\n",
      "  (encoder): VisionTransformer(\n",
      "    (patch_embed): PatchEmbed(\n",
      "      (proj): Conv2d(3, 384, kernel_size=(16, 16), stride=(16, 16))\n",
      "    )\n",
      "    (pos_drop): Dropout(p=0.0, inplace=False)\n",
      "    (blocks): ModuleList(\n",
      "      (0): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): Identity()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (1): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (2): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (3): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (4): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (5): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (6): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (7): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (8): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (9): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (10): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "      (11): Block(\n",
      "        (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (attn): Attention(\n",
      "          (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
      "          (attn_drop): Dropout(p=0.0, inplace=False)\n",
      "          (proj): Linear(in_features=384, out_features=384, bias=True)\n",
      "          (proj_drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (drop_path): DropPath()\n",
      "        (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "        (mlp): Mlp(\n",
      "          (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
      "          (act): GELU(approximate=none)\n",
      "          (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
      "          (drop): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n",
      "    (head): Linear(in_features=384, out_features=2, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(att_net)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-17T00:25:16.077891800Z",
     "start_time": "2024-06-17T00:25:16.037635200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "from torchvision.transforms import transforms\n",
    "import os\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "trans = transforms.Compose([\n",
    "            transforms.Resize(256),\n",
    "            transforms.CenterCrop(224),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(np.array([0.485, 0.456, 0.406]), np.array([0.229, 0.224, 0.225]))\n",
    "        ])\n",
    "imgs = []\n",
    "for img_name in os.listdir('img'):\n",
    "   img = Image.open(os.path.join(os.getcwd(),'img',img_name)).convert('RGB')\n",
    "   img = trans(img)\n",
    "   imgs.append(img)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-17T00:25:26.056968200Z",
     "start_time": "2024-06-17T00:25:24.811304100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "imgs = torch.stack([t for t in imgs],dim=0)\n",
    "print(imgs.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-17T00:25:28.313731200Z",
     "start_time": "2024-06-17T00:25:28.279696200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "imgs = imgs.to('cuda')\n",
    "att_net = att_net.to('cuda')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-17T00:25:32.806404300Z",
     "start_time": "2024-06-17T00:25:31.487574500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "output = att_net(imgs)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T02:00:38.110309200Z",
     "start_time": "2024-06-12T02:00:32.110331500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [],
   "source": [
    "attns = att_net.attentions\n",
    "with torch.no_grad():\n",
    "    result = torch.eye(attns[0].size(-1)-1).unsqueeze(0).to(attns[0].device)  # (1, L, L)\n",
    "    for attn in attns:\n",
    "        attn_fused = attn.min(1)[0][:, 1:, 1:]\n",
    "        _, indices = attn_fused.topk(int(attn_fused.size(-1) * 0.9), -1, False)\n",
    "        attn_fused = attn_fused.scatter_(-1, indices, 0)\n",
    "        I = torch.eye(attn_fused.size(-1)).unsqueeze(0).to(attn_fused.device)\n",
    "        # a = (attn_fused + I) / 2.\n",
    "        # a = (attn_fused + I)\n",
    "        a = a / torch.sum(a,dim=-1,keepdim=True)\n",
    "        result = a @ result\n",
    "imp = result.mean(1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T03:04:29.337548Z",
     "start_time": "2024-06-12T03:04:29.229208Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [],
   "source": [
    "def imp_to_focus(imp, P):\n",
    "    _, ids_shuffle = torch.sort(imp, descending=True, dim=1)\n",
    "    ids_restore = torch.argsort(ids_shuffle, dim=1)\n",
    "    focus = torch.ones_like(imp)\n",
    "    focus[:, P:] = 0\n",
    "    focus = torch.gather(focus, dim=1, index=ids_restore)\n",
    "\n",
    "    focus = imp * focus\n",
    "    focus = focus * P / focus.sum(-1, keepdim=True)\n",
    "    return focus"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T03:04:46.523993700Z",
     "start_time": "2024-06-12T03:04:46.513954700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [],
   "source": [
    "score = imp.numpy()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T03:04:48.440736500Z",
     "start_time": "2024-06-12T03:04:48.429772600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 196])\n",
      "tensor(112.0000)\n"
     ]
    }
   ],
   "source": [
    "print()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T02:25:56.804276500Z",
     "start_time": "2024-06-12T02:25:56.790321500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166\n"
     ]
    }
   ],
   "source": [
    "n = 0\n",
    "sum_s = 0.\n",
    "ids = []\n",
    "sorted_indices = np.argsort(score[7])[::-1]\n",
    "for idx in sorted_indices:\n",
    "    ids.append(idx)\n",
    "    sum_s += score[7][idx]\n",
    "    n += 1\n",
    "    if sum_s>0.95:\n",
    "        break\n",
    "print(n)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T03:04:49.203105600Z",
     "start_time": "2024-06-12T03:04:49.184060Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [],
   "source": [
    "# 做idx和图像中patch的位置映射\n",
    "def idx_map_loc(idx):\n",
    "    square_x = (idx%14)*16\n",
    "    square_y = int(idx/14)*16\n",
    "    square_width = 16\n",
    "    square_height = 16\n",
    "    return [square_x, square_x + square_width, square_x + square_width, square_x, square_x],\\\n",
    "           [square_y, square_y, square_y + square_height, square_y + square_height, square_y]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T03:04:50.502504100Z",
     "start_time": "2024-06-12T03:04:50.483567Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 读取图像\n",
    "image = cv2.imread('img/n0153282900000090.jpg')\n",
    "image = cv2.resize(image,[224,224])\n",
    "# 转换为灰度图像\n",
    "gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# 绘制热力图\n",
    "plt.imshow(gray_image, cmap='hot')\n",
    "\n",
    "# 添加颜色条\n",
    "plt.colorbar()\n",
    "\n",
    "for idx in ids[:20]:\n",
    "    x,y = idx_map_loc(idx)\n",
    "    plt.plot(x,y, color='red')\n",
    "# plt.plot([0, 16, 16 , 0, 0],[0, 0, 16,16, 0],color='red')\n",
    "# plt.plot([16, 32, 32 , 16, 16],[0, 0, 16,16, 0],color='red')\n",
    "# plt.plot(idx_map_loc(0), color='red')\n",
    "#\n",
    "# plt.plot(x,y,color='red')\n",
    "# 显示图形\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T03:05:08.445429300Z",
     "start_time": "2024-06-12T03:05:08.240191600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[0.20323202 0.90297099]\n",
      "   [0.28564967 0.9808154 ]]]\n",
      "\n",
      "\n",
      " [[[0.06518012 0.88367592]\n",
      "   [0.30491432 0.92735517]]]]\n",
      "[[[[1.20323202 0.90297099]\n",
      "   [0.28564967 1.9808154 ]]]\n",
      "\n",
      "\n",
      " [[[1.06518012 0.88367592]\n",
      "   [0.30491432 1.92735517]]]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 创建一个示例四维数组\n",
    "array = np.random.rand(2, 1, 2, 2)  # 假设数组大小为 (2, 3, 4, 4)\n",
    "print(array)\n",
    "# 获取第三、第四维度的大小\n",
    "dim3, dim4 = array.shape[2], array.shape[3]\n",
    "\n",
    "# 创建一个单位矩阵\n",
    "unit_matrix = np.eye(dim4)\n",
    "\n",
    "# 在第三、第四维度上加上单位矩阵\n",
    "array += unit_matrix\n",
    "\n",
    "print(array)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T00:38:02.195116100Z",
     "start_time": "2024-06-12T00:38:02.178107500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3)\n"
     ]
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-11T14:54:04.792901700Z",
     "start_time": "2024-06-11T14:54:04.780900200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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